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A Model-Aware Comprehensive Tool for Battery Energy Storage System Sizing

Author

Listed:
  • Matteo Spiller

    (Politecnico di Milano—Department of Energy, Via Lambruschini 4a, 20156 Milano, Italy)

  • Giuliano Rancilio

    (Politecnico di Milano—Department of Energy, Via Lambruschini 4a, 20156 Milano, Italy)

  • Filippo Bovera

    (Politecnico di Milano—Department of Energy, Via Lambruschini 4a, 20156 Milano, Italy)

  • Giacomo Gorni

    (Eni S.p.A., Renewable, New Energies and Material Science Research Center, Via Fauser 4, 28100 Novara, Italy)

  • Stefano Mandelli

    (Plenitude, Via Giuseppe Ripamonti 85, 20141 Milano, Italy)

  • Federico Bresciani

    (Eni S.p.A., Renewable, New Energies and Material Science Research Center, Via Fauser 4, 28100 Novara, Italy)

  • Marco Merlo

    (Politecnico di Milano—Department of Energy, Via Lambruschini 4a, 20156 Milano, Italy)

Abstract

This paper presents a parametric procedure to size a hybrid system consisting of renewable generation (wind turbines and photovoltaic panels) and Battery Energy Storage Systems (BESS). To cope with the increasing installation of grid-scale BESS, an innovative, fast and flexible procedure for evaluating an efficient size for this asset has been developed. The tool exploits a high-fidelity empirical model to assess stand-alone BESS or hybrid power plants under different service stacking configurations. The economic performance has been evaluated considering the revenue stacking that occurs when participating in up to four distinct energy markets and the degradation of the BESS performances due to both cycle- and calendar-aging. The parametric nature of the tool enables the investigation of a wide range of system parameters, including novel BESS control logic, market prices, and energy production. The presented outcomes detail the techno-economic performances of a hybrid system over a 20-year scenario, proposing a sensitivity analysis of both technical and economic parameters. The case study results highlight the necessity of steering BESS investment towards the coupling of RES and accurate planning of the service stacking. Indeed, the implementation of a storage system in an energy district improves the internal rate of return of the project by up to 10% in the best-case scenario. Moreover, accurate service stacking has shown a boost in revenues by up to 44% with the same degradation.

Suggested Citation

  • Matteo Spiller & Giuliano Rancilio & Filippo Bovera & Giacomo Gorni & Stefano Mandelli & Federico Bresciani & Marco Merlo, 2023. "A Model-Aware Comprehensive Tool for Battery Energy Storage System Sizing," Energies, MDPI, vol. 16(18), pages 1-24, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6546-:d:1237929
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    References listed on IDEAS

    as
    1. Mercier, Thomas & Olivier, Mathieu & De Jaeger, Emmanuel, 2023. "The value of electricity storage arbitrage on day-ahead markets across Europe," Energy Economics, Elsevier, vol. 123(C).
    2. Matteo Moncecchi & Claudio Brivio & Stefano Mandelli & Marco Merlo, 2020. "Battery Energy Storage Systems in Microgrids: Modeling and Design Criteria," Energies, MDPI, vol. 13(8), pages 1-18, April.
    3. Nataliia Shamarova & Konstantin Suslov & Pavel Ilyushin & Ilia Shushpanov, 2022. "Review of Battery Energy Storage Systems Modeling in Microgrids with Renewables Considering Battery Degradation," Energies, MDPI, vol. 15(19), pages 1-18, September.
    4. Nicola Campagna & Vincenzo Castiglia & Rosario Miceli & Rosa Anna Mastromauro & Ciro Spataro & Marco Trapanese & Fabio Viola, 2020. "Battery Models for Battery Powered Applications: A Comparative Study," Energies, MDPI, vol. 13(16), pages 1-26, August.
    5. Nebuloni, Riccardo & Meraldi, Lorenzo & Bovo, Cristian & Ilea, Valentin & Berizzi, Alberto & Sinha, Snigdh & Tamirisakandala, Raviteja Bharadwaj & Raboni, Pietro, 2023. "A hierarchical two-level MILP optimization model for the management of grid-connected BESS considering accurate physical model," Applied Energy, Elsevier, vol. 334(C).
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